Draws a random (sub)sample (with or without replacement).
Usage
resample_uniform(object, ...)
# S4 method for class 'numeric'
resample_uniform(object, n, size = length(object), replace = FALSE, ...)Value
A numeric matrix with n rows and size columns.
See also
Other resampling methods:
bootstrap(),
jackknife(),
resample_multinomial()
Examples
## Uniform distribution
x <- rnorm(20)
resample_uniform(x, n = 10)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] -2.06991393 -0.4365199 1.28670524 0.06931626 -1.2046064 1.1456325
#> [2,] 0.11491376 1.1456325 0.06931626 -1.20460638 -0.1646107 0.0199721
#> [3,] 0.01997210 1.1686415 -1.31716424 1.01909699 0.1149138 -1.2046064
#> [4,] 0.06931626 -1.4785532 1.00179059 -0.52874430 1.0016327 -1.3171642
#> [5,] 0.06931626 1.0017906 -0.95685271 0.11491376 -2.0699139 1.1456325
#> [6,] 1.01909699 -0.9568527 -0.43651990 0.63281310 -1.4785532 -2.0699139
#> [7,] -2.06991393 1.0017906 1.28670524 1.16864147 -0.9568527 1.0016327
#> [8,] 0.01997210 -2.0699139 -0.43651990 0.11491376 -0.1646107 -1.2046064
#> [9,] 1.01909699 1.1686415 -2.06991393 1.00163270 -0.1646107 1.2867052
#> [10,] -1.31716424 -1.4785532 1.16864147 0.11491376 1.1456325 -1.2046064
#> [,7] [,8] [,9] [,10] [,11] [,12]
#> [1,] -1.47855323 -1.85149913 1.0190970 0.01997210 -0.52874430 -1.1695096
#> [2,] 1.00163270 -0.43651990 -2.0699139 -0.52874430 1.28670524 -1.1695096
#> [3,] 1.00179059 -1.85149913 -0.9568527 -0.43651990 0.06931626 -0.5287443
#> [4,] 1.16864147 0.63281310 1.2867052 -1.16950957 0.11491376 -0.4365199
#> [5,] 1.00163270 -0.16461072 -1.1695096 1.01909699 -1.85149913 1.1686415
#> [6,] 0.06931626 0.01997210 1.2867052 -1.20460638 -0.16461072 0.1149138
#> [7,] -0.52874430 0.63281310 1.0190970 -1.20460638 -0.43651990 -1.8514991
#> [8,] -1.31716424 0.06931626 -1.1695096 1.00163270 -1.85149913 -1.4785532
#> [9,] 1.14563254 -0.95685271 -1.3171642 -1.85149913 0.11491376 0.6328131
#> [10,] 1.00163270 -2.06991393 -1.1695096 0.06931626 1.01909699 -0.4365199
#> [,13] [,14] [,15] [,16] [,17] [,18]
#> [1,] -0.1646107 1.16864147 -1.31716424 0.6328131 1.0017906 -0.9568527
#> [2,] -1.4785532 1.01909699 -1.31716424 1.1686415 1.0017906 0.6328131
#> [3,] 0.6328131 1.14563254 1.28670524 -1.4785532 -0.1646107 -2.0699139
#> [4,] -1.2046064 1.01909699 1.14563254 -0.1646107 -1.8514991 -2.0699139
#> [5,] -1.4785532 -1.31716424 -0.43651990 1.2867052 -0.5287443 -1.2046064
#> [6,] 1.0016327 1.14563254 -1.85149913 -1.3171642 -0.5287443 1.0017906
#> [7,] 0.0199721 0.06931626 -1.31716424 0.1149138 -0.1646107 1.1456325
#> [8,] 1.1456325 -0.95685271 1.00179059 0.6328131 -0.5287443 1.0190970
#> [9,] -0.4365199 1.00179059 0.06931626 -1.4785532 -1.1695096 -0.5287443
#> [10,] -0.5287443 -0.16461072 -0.95685271 1.2867052 0.0199721 1.0017906
#> [,19] [,20]
#> [1,] 1.0016327 0.1149138
#> [2,] -1.8514991 -0.9568527
#> [3,] 1.0016327 -1.1695096
#> [4,] 0.0199721 -0.9568527
#> [5,] 0.0199721 0.6328131
#> [6,] -1.1695096 1.1686415
#> [7,] -1.4785532 -1.1695096
#> [8,] 1.2867052 1.1686415
#> [9,] 0.0199721 -1.2046064
#> [10,] -1.8514991 0.6328131
## Multinomial distribution
x <- sample(1:100, 20, TRUE)
resample_multinomial(x, n = 10)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] 52 20 6 60 12 72 18 35 45 48 67 80 29
#> [2,] 42 18 5 58 19 78 23 35 39 51 74 75 25
#> [3,] 63 12 6 54 22 66 21 33 48 51 81 92 22
#> [4,] 44 17 2 64 16 55 22 32 38 50 76 76 26
#> [5,] 45 23 4 48 23 75 19 37 44 53 73 98 29
#> [6,] 47 24 3 64 13 68 18 45 32 59 84 96 30
#> [7,] 61 18 13 56 28 75 18 31 47 48 60 75 33
#> [8,] 36 19 4 53 18 81 22 44 34 51 93 77 32
#> [9,] 57 20 8 50 18 63 24 26 42 62 83 93 20
#> [10,] 51 16 10 62 4 65 21 34 51 43 72 90 32
#> [,14] [,15] [,16] [,17] [,18] [,19] [,20]
#> [1,] 33 65 77 56 26 58 12
#> [2,] 50 60 68 45 21 68 17
#> [3,] 37 34 99 54 16 48 12
#> [4,] 47 59 86 60 23 69 9
#> [5,] 36 54 73 48 16 65 8
#> [6,] 40 54 73 38 26 51 6
#> [7,] 30 59 85 51 17 55 11
#> [8,] 42 52 86 51 18 53 5
#> [9,] 40 52 82 51 28 47 5
#> [10,] 46 57 83 47 18 63 6